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In this work, we are interested in the stability and robustness of theparameter estimation in the Zero-Inflated Bernoulli (ZIBer) model, when thesusceptible probability (SP) model is modeled by numerous different binarymodels: logit, probit, cloglog and generalized extreme value (GEV). To addressthis problem, we propose the maximum likelihood estimation (MLE) method tocheck its performance when different SP models are considered. Based onnumerical evidences through simulation studies and the analysis of a real dataset, it can be seen that the MLE approach has provided accurate and reliableinferences. In addition, it can also be seen that for the empirical analysis,the probit-ZIBer model is probably more suitable for the fishing data set thanthe other models considered in this study. Besides, the results obtained in theexperimental analysis are also very consistent, compatible and very meaningfulin practice. It will help us to understand the importance of increasingproduction while fishing.

A simulation-based study of Zero-inflated Bernoulli model with various models for the susceptible probability